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OneAPI Deep Neural Network Library: Difference between revisions

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'''oneAPI Deep Neural Network Library''' ('''oneDNN''') is a performance [[Library (computing)|library]] of basic building blocks for [[Deep_learning|deep learning]] applications.<ref>{{cite web |url=https://github.com/oneapi-src/oneDNN |title=github oneDNN}}</ref><ref>{{cite web |title=oneDNN 2.2 Released With More Optimizations For Alder Lake, Sapphire Rapids |url=https://www.phoronix.com/scan.php?page=news_item&px=Intel-oneDNN-2.2 |website=Phoronix}}</ref> oneDNN is an open-source and cross-platform library element of [[OneAPI_(compute_acceleration)|oneAPI]]<ref>{{cite web |url=https://www.oneapi.io/ |title=oneAPI}}</ref>.  
'''oneAPI Deep Neural Network Library''' ('''oneDNN''') is a performance [[Library (computing)|library]] of basic building blocks for [[Deep learning|deep learning]] applications.<ref>{{cite web |title=oneAPI Deep Neural Network Library |url=https://spec.oneapi.io/versions/0.5.0/oneAPI/Elements/onednn/onednn_root.html |website=oneAPI}}</ref> oneDNN is an open-source and cross-platform library element of [[OneAPI (compute acceleration)|oneAPI]]<ref>{{cite web |url=https://www.oneapi.io/ |title=oneAPI}}</ref><ref>{{cite book |title=Deep Learning Systems |publisher=Morgan & Claypool Publishers |location=Google Books |isbn=9781681739670 |url=https://www.google.com/books/edition/Deep_Learning_Systems/OKAFEAAAQBAJ}}</ref>.  


oneDNN is useful for deep learning application and framework developers to improve application performance<ref>{{cite web |title=Software AI accelerators: AI performance boost for free |url=https://venturebeat.com/2021/09/22/software-ai-accelerators-ai-performance-boost-for-free/ |website=Venture Beat}}</ref><ref>{{cite web |title=oneDNN hits v1.4 |url=https://devclass.com/2020/04/20/intel-onednn-hits-v1-4-with-drumroll-better-intel-support/ |website=Dev Class}}</ref> using the same API for both CPUs and GPUs. It abstracts out the specific instruction set and other complexities of performance optimizations. The oneDNN library is available for [[Windows]], [[Linux]] and [[macOS]] [[operating system]]s.
oneDNN is useful for deep learning application and framework developers to improve application performance <ref>{{cite web |title=oneDNN benchmarks |url=https://openbenchmarking.org/test/pts/onednn |website=openbenchmarking}}</ref><ref>{{cite web |title=Developing Deep Learning Frameworks for Exascale |url=https://www.hpcwire.com/off-the-wire/aurora-software-development-developing-deep-learning-frameworks-for-exascale/ |website=HPC Wire}}</ref><ref>{{cite web |title=Optimizing Inference Performance of Transformers on CPUs |url=https://arxiv.org/pdf/2102.06621.pdf |website=arXiv |publisher=Cornell University}}</ref>using the same API for both CPUs and GPUs. It abstracts out the specific instruction set and other complexities of performance optimizations. The oneDNN library is available for [[Windows]], [[Linux]] and [[macOS]] operating systems.


oneDNN provides optimizations for popular frameworks including:
oneDNN provides optimizations for popular deep learning frameworks including:
* TensorFlow*<ref>{{cite web |title=Leverage Intel Deep Learning Optimizations in TensorFlow |url=https://medium.com/intel-analytics-software/leverage-intel-deep-learning-optimizations-in-tensorflow-129faa80ee07 |website=Medium}}</ref><ref>{{cite web |title=TensorFlow 2.5.0 Released |url=https://analyticsindiamag.com/tensorflow-2-5-0-released-all-major-updates-features/ |website=Analytics India Magazine}}</ref>
* TensorFlow*<ref>{{cite web |title=TensorFlow and oneDNN in Partnership |url=https://www.oneapi.io/wp-content/uploads/sites/74/Penporn-Koanantakool-TensorFlow-and-oneDNN-in-Partnership.pdf |website=oneAPI}}</ref>
* PyTorch*<ref>{{cite web |title=Accelerate PyTorch with IPEX and oneDNN |url=https://medium.com/pytorch/accelerate-pytorch-with-ipex-and-onednn-using-intel-bf16-technology-dca5b8e6b58f |website=Medium}}</ref><ref>{{cite web |title=Optimize the Latest Deep Learning Workloads using Intel-optimized PyTorch |url=https://www.alcf.anl.gov/events/optimize-latest-deep-learning-workloads-using-intel-optimized-pytorch |website=Argonne Leadership Computing Facility}}</ref>
* PyTorch*<ref>{{cite web |title=Optimize the Latest Deep Learning Workloads using Intel-optimized PyTorch |url=https://www.alcf.anl.gov/events/optimize-latest-deep-learning-workloads-using-intel-optimized-pytorch |website=Argonne Leadership Computing Facility}}</ref>


==History==
==History==
*oneDNN started as the Deep Neural Networks (DNN) component of [[Math_Kernel_Library|Intel MKL library]] first released in [https://software.intel.com/content/www/us/en/develop/articles/intel-math-kernel-library-release-notes-and-new-features.html Intel MKL 2017].
*oneDNN started as the Deep Neural Networks (DNN) component of [[Math Kernel Library|Intel MKL library]] first released in [https://software.intel.com/content/www/us/en/develop/articles/intel-math-kernel-library-release-notes-and-new-features.html Intel MKL 2017].
*Considering fast development in the field and opportunity for industry collaboration Intel launched an open source project named [https://github.com/oneapi-src/oneDNN/releases/tag/v0.1/ Intel MKL-DNN] on August 28, 2016.
*Considering fast development in the field and opportunity for industry collaboration Intel launched an open source project named [https://github.com/oneapi-src/oneDNN/releases/tag/v0.1/ Intel MKL-DNN] on August 28, 2016.
*The [[Math_Kernel_Library|Intel MKL]] DNN component was deprecated in [https://software.intel.com/content/www/us/en/develop/articles/intel-math-kernel-library-release-notes-and-new-features.html Intel MKL 2019] and removed in [https://software.intel.com/content/www/us/en/develop/articles/intel-math-kernel-library-release-notes-and-new-features.html Intel MKL 2020].
*The [[Math Kernel Library|Intel MKL]] DNN component was deprecated in [https://software.intel.com/content/www/us/en/develop/articles/intel-math-kernel-library-release-notes-and-new-features.html Intel MKL 2019] and removed in [https://software.intel.com/content/www/us/en/develop/articles/intel-math-kernel-library-release-notes-and-new-features.html Intel MKL 2020].
*MKL-DNN reached production maturity with [https://github.com/oneapi-src/oneDNN/releases/tag/v1.0 MKL-DNN v1.0] on Jul 12, 2019.  
*MKL-DNN reached production maturity with [https://github.com/oneapi-src/oneDNN/releases/tag/v1.0 MKL-DNN v1.0] on Jul 12, 2019.  
*Arm contributed support for [[AArch64|AArch64 processors]] in [https://github.com/oneapi-src/oneDNN/releases/tag/v1.5 oneDNN v1.5]
*Arm contributed support for [[AArch64|AArch64 processors]] in [https://github.com/oneapi-src/oneDNN/releases/tag/v1.5 oneDNN v1.5]
*The library became a part of [[OneAPI_(compute_acceleration)#oneAPI_libraries|oneAPI initiative]] and renamed to oneDNN in [https://github.com/oneapi-src/oneDNN/releases/tag/v2.0 oneDNN v2.0] on Dec 8, 2020.
*The library became a part of [[OneAPI (compute acceleration)#oneAPI libraries|oneAPI initiative]] and renamed to oneDNN in [https://github.com/oneapi-src/oneDNN/releases/tag/v2.0 oneDNN v2.0] on Dec 8, 2020.
*Intel branded the binary distribution [https://software.intel.com/content/www/us/en/develop/tools/oneapi/components/onednn.html Intel® oneAPI Deep Neural Network Library] (oneDNN) launched as part of [https://software.intel.com/content/www/us/en/develop/tools/oneapi.html Intel® oneAPI] 2021 release.
*Intel branded the binary distribution [https://software.intel.com/content/www/us/en/develop/tools/oneapi/components/onednn.html Intel® oneAPI Deep Neural Network Library] (oneDNN) launched as part of [https://software.intel.com/content/www/us/en/develop/tools/oneapi.html Intel® oneAPI] 2021 release.
*[[Codeplay]] contributed support for [[NVIDIA]] GPUs in [https://github.com/oneapi-src/oneDNN/releases/tag/v2.1 oneDNN v2.1]
*[[Codeplay]] contributed support for [[NVIDIA]] GPUs in [https://github.com/oneapi-src/oneDNN/releases/tag/v2.1 oneDNN v2.1]

Latest revision as of 14:16, 13 October 2025






oneAPI Deep Neural Network Library
Repositorygithub.com/oneapi-src/oneDNN
Engine
    Operating systemMicrosoft Windows, Linux, macOS
    PlatformCross-platform
    TypeOpen-source Library
    Websitespec.oneapi.io/versions/latest/elements/oneDNN/source/index.html

    Search OneAPI Deep Neural Network Library on Amazon.

    oneAPI Deep Neural Network Library (oneDNN) is a performance library of basic building blocks for deep learning applications.[1] oneDNN is an open-source and cross-platform library element of oneAPI[2][3].

    oneDNN is useful for deep learning application and framework developers to improve application performance [4][5][6]using the same API for both CPUs and GPUs. It abstracts out the specific instruction set and other complexities of performance optimizations. The oneDNN library is available for Windows, Linux and macOS operating systems.

    oneDNN provides optimizations for popular deep learning frameworks including:

    History

    License

    Apache License 2.0

    References

    1. ↑ "oneAPI Deep Neural Network Library". oneAPI.
    2. ↑ "oneAPI".
    3. ↑ Deep Learning Systems. Google Books: Morgan & Claypool Publishers. ISBN 9781681739670. Search this book on
    4. ↑ "oneDNN benchmarks". openbenchmarking.
    5. ↑ "Developing Deep Learning Frameworks for Exascale". HPC Wire.
    6. ↑ "Optimizing Inference Performance of Transformers on CPUs" (PDF). arXiv. Cornell University.
    7. ↑ "TensorFlow and oneDNN in Partnership" (PDF). oneAPI.
    8. ↑ "Optimize the Latest Deep Learning Workloads using Intel-optimized PyTorch". Argonne Leadership Computing Facility.

    External links


    This article "OneAPI Deep Neural Network Library" is from Wikipedia. The list of its authors can be seen in its historical and/or the page Edithistory:OneAPI Deep Neural Network Library. Articles copied from Draft Namespace on Wikipedia could be seen on the Draft Namespace of Wikipedia and not main one.


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